In recent decades advanced driver assistance systems (ADAS) have been developed to help and assist drivers and prevent accidents. ADAS provide a more comfortable and safer driving experience by supporting human awareness and actions with exact machine tasks and warnings. These reduce significantly the amount of accidents caused by driver errors.
ADAS are usually based on proximity sensors, e.g., radar, laser and/or ultrasound, camera systems, global positioning systems (GPS), car-to-car and car-to-infrastructure systems. Proximity sensors are used to develop systems such as adaptive cruise control (ACC), automatic parking, lane change assistance, blind spot detection (BSD) systems, emergency brake assist (EBA), etc. A precise world model is among the essential requirements of a successful implementation of the ADAS, which would significantly reduce the complication of tasks such as navigation, path planning, and obstacle avoidance.
Distinction of traversable and non-traversable objects is an important topic of the ADAS. It provides important information as to where a vehicle can drive under special conditions. For example, a curb is a traversable obstacle which is seen by a radar, but does not necessary limit the drivable area as, for example, the vehicle can cross the curb to park on a sidewalk. If one interprets any radar detections directly as non-traversable obstacles, this would yield wrong understanding of the environment.
Usually, to distinguish between traversable and non-traversable obstacles, one needs height information. Automotive radars without special elevation measurement function (two-dimensional or 2D radars) do not provide height information, and three-dimensional or 3D radars with full resolution of vertical angle are still expensive.